In the fields of computational modeling and high performance computing, modeling platforms are known which contain a modeling engine to receive a variety of modeling inputs, and then generate a precise modeled output based on those inputs. In conventional modeling platforms, the set of inputs are precisely known, and the function applied to the modeling inputs is precisely known, but the ultimate results produced by the modeling engine are not known until the input data is supplied and the modeling engine is run. For example, in an econometric modeling platform, inputs for a particular industry like housing can be fed into a modeling engine. Those inputs can include, for instance, prevailing finance rates, employment rates, average new-home costs, costs of building materials, rate of inflation, and other economic or other variables that can be fed into the modeling engine which is programmed or configured to accept those inputs, apply a function or other processing to those inputs, and generate an output such as projected new-home sales for a given period of time. Those results can then be used to analyze or forecast other details related to the subject industry, such as predicted sector profits or employment.
In many real-life analytic applications, however, the necessary inputs for a given subject or study may not be known, while, at the same time, a desired or target output may be known or estimated with some accuracy. For instance, the research and development (R&D) department of a given corporation may be fixed at the beginning of a year or other budget cycle, but the assignment or allocation of that available amount of funds to different research teams or product areas may not be specified by managers or others. In such a case, an analyst may have to manually estimate and “back out” distributions of budget funds to different departments to begin to work out a set of component funding amounts that will, when combined, produce the already-known overall R&D or other budget. In performing that interpolation, the analyst may or may not be in possession of some departmental component budgets which have themselves also been fixed, or may or may not be in possession of the computation function which will appropriately sum or combine all component funds to produce the overall predetermined target budget. Adjustment of one component amount by hand may cause or suggest changes in other components in a ripple effect, which the analyst will then have to examine or account for in a further iteration of the same manual estimates.
In cases where an interpolation study is conducted and a collection of series of interpolated is generated, the analyst or other operator may be presented with a choice or decision between different alternative series that produce a desired output. For instance, a manufacturer may conduct a study or analysis to determine various combinations of chemical ingredients that may produce a given grade of industrial solvent at a selected price point. The analysis may not, however, provide the user with insight regarding which components may be most dramatically changed between different formulations, so that the chemical properties of the desired product can be effectively tracked or estimated. In such a scenario or others, the manufacturer or producer may wish to limit the change in ingredient X to be no more than 10% of ingredient Y, regardless of the eventual total complement of ingredients. Other situations may require or benefit from differential analytics.
It may be desirable to provide systems and methods for tracking differential changes in conformal data input sets, in which the user of an interpolation tool can generate and navigate various alternative series of data based on a set of differential criteria.